Paper Info
Title
Imu-Assisted Nearest Neighbor Selection For Real-Time Wifi Fingerprinting Positioning
Abstract
This paper presents a nearest neighbor selection algorithm for real-time WiFi fingerprinting positioning with the assist of inertial measurement unit (IMU) measurements. The WiFi fingerprinting positioning using received signal strength (RSS) measurements suffers from the RSS variation problem. Due to this problem, reference points that are irrelevant to the user's position are selected, and the positioning accuracy decreases. To overcome the RSS variation problem, we propose an IMU-assisted nearest neighbor selection algorithm that filters out irrelevant reference points based on the position prediction with IMU measurements. The proposed algorithm was evaluated and compared with the conventional K-nearest neighbors (KNN) selection and the IMU-based dead-reckoning positioning in a real indoor environment. The experimental results showed that the average positioning error of the proposed algorithm was 2.41 m, whereas those of the KNN-based fingerprinting algorithm and the IMU-based dead-reckoning positioning were 3.57 m and 15.27 m.
Year
Venue
Field
2014
2014 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN)
k-nearest neighbors algorithm,Computer vision,Wireless,Selection algorithm,Prediction algorithms,Artificial intelligence,Inertial measurement unit,Signal strength,Engineering,RSS,Signal processing algorithms
DocType
ISSN
Citations 
Conference
2162-7347
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
myungjun jin110.69
Bonhyun Koo2355.28
Sangwoo Lee3424.85
Chansik Park4536.03
Min Joon Lee500.68
Sunwoo Kim66611.00